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TitleBuilding molecular model series from heterogeneous CryoEM structures using Gaussian mixture models and deep neural networks.
Journal, issue, pagesCommun Biol, Vol. 8, Issue 1, Page 798, Year 2025
Publish dateMay 25, 2025
AuthorsMuyuan Chen /
PubMed AbstractCryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can ...Cryogenic electron microscopy (CryoEM) produces structures of macromolecules at near-atomic resolution. However, building molecular models with good stereochemical geometry from those structures can be challenging and time-consuming, especially when many structures are obtained from datasets with conformational heterogeneity. Here we present a model refinement protocol that automatically generates series of molecular models from CryoEM datasets, which describe the dynamics of the macromolecular system and have near-perfect geometry scores. This method makes it easier to interpret the movement of the protein complex from heterogeneity analysis and to compare the structural dynamics observed from CryoEM data with results from other experimental and simulation techniques.
External linksCommun Biol / PubMed:40415012 / PubMed Central
MethodsEM (single particle)
Resolution4.2 Å
Structure data

PDB-9ogk:
Refinement of PDB-3J5R against EMD-8117 using EMAN2
Method: ELECTRON MICROSCOPY / Resolution: 4.2 Å

Source
  • rattus norvegicus (Norway rat)
KeywordsMEMBRANE PROTEIN / TRPV1

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